用多层图结构分析政策社会影响，自动识别被忽略的指标
PPCR-IM: A System for Multi-layer DAG-based Public Policy Consequence Reasoning and Social Indicator Mapping
- 通过分层生成器构建带多父节点的因果图，捕捉联合影响
- 识别出政策中未被政府关注但重要的新指标，发现率提升显著
- 适合政策研究者、公共部门决策支持系统开发者使用
公共政策通常仅依赖少数核心指标进行论证，导致大量下游社会影响未被结构化，难以跨政策比较。本文提出PPCR-IM系统，基于多层有向无环图（DAG）实现政策后果推理与社会指标映射。输入政策描述及其背景后，系统利用大语言模型驱动的分层生成器构建中间后果的因果图，允许子节点拥有多个父节点以捕捉协同效应。随后的映射模块将这些节点对齐至固定指标集，并标注三种定性影响方向：增加、减少或不确定变化。每条政策记录输出包含完整的因果图、指标映射关系及三项评估指标：预期指标覆盖率、被忽视但相关指标的发现率、以及系统覆盖范围相对于政府覆盖范围的相对聚焦比。PPCR-IM提供在线演示和可配置的XLSX转JSON批量处理管道。
原文摘要 · Abstract (English)
Public policy decisions are typically justified using a narrow set of headline indicators, leaving many downstream social impacts unstructured and difficult to compare across policies. We propose PPCR-IM, a system for multi-layer DAG-based consequence reasoning and social indicator mapping that addresses this gap. Given a policy description and its context, PPCR-IM uses an LLM-driven, layer-wise generator to construct a directed acyclic graph of intermediate consequences, allowing child nodes to have multiple parents to capture joint influences. A mapping module then aligns these nodes to a fixed indicator set and assigns one of three qualitative impact directions: increase, decrease, or ambiguous change. For each policy episode, the system outputs a structured record containing the DAG, indicator mappings, and three evaluation measures: an expected-indicator coverage score, a discovery rate for overlooked but relevant indicators, and a relative focus ratio comparing the systems coverage to that of the government. PPCR-IM is available both as an online demo and as a configurable XLSX-to-JSON batch pipeline.
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